Despite the rapid advancement of LLMs, their performance on linguistically and culturally diverse minority languages within a unified national context remains underexplored.We present CMiLBench, a collection of hierarchical multitask benchmarks designed to translate theoretical notions of diversity in unity (in Chinese: "美美与共") into practical evaluation for three representative Chinese minority languages: Tibetan, Mongolian, and Uyghur.CMiLBench comprises 24,663 instances across 5 difficulty levels and 17 tasks spanning foundational ability, cultural specificity, and safety alignment.We adopt existing dataset adaptation, minority knowledge construction, and high-resource benchmark translation to construct CMiLBench.We assess 14 state-of-the-art commercial and open-source LLMs with a hybrid framework that integrates automatic metrics and LLM-as-a-Judge scoring.The comparative experimental results reveal the gap between theoretical capability and practical utility.CMiLBench serves as a foundational and scalable evaluation resource to bridge the digital language divide and promote the informatization and intelligentization of lowresource Chinese minority languages.